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检索条件"任意字段=8th International Conference on Machine Learning and Data Mining in Pattern Recognition, MLDM 2012"

共 106 条记录，以下是11-20 订阅

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Deep Reinforcement learning for Exact Combinatorial Optimization: learning to Branch 26

Deep Reinforcement Learning for Exact Combinatorial Optimiza...

引用

26th international conference on pattern recognition / 8th international Workshop on Image mining - theory and Applications (IMTA)

作者： Zhang, Tianyu Banitalebi-Dehkordi, Amin Zhang, Yong Univ Alberta Edmonton AB Canada Huawei Technol Canada Co Ltd Vancouver BC Canada

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Branch-and-bound is a systematic enumerative method for combinatorial optimization, where the performance highly relies on the variable selection strategy. State-of-theart handcrafted heuristic strategies suffer from relatively slow inference time for each selection, while the current machine learning methods require a significant amount of labeled data. We propose a new approach for solving the data labeling and inference latency issues in combinatorial optimization based on the use of the reinforcement learning (RL) paradigm. We use imitation learning to bootstrap an RL agent and then use Proximal Policy Optimization (PPO) to further explore global optimal actions. then, a value network is used to run Monte-Carlo tree search (MCTS) to enhance the policy network. We evaluate the performance of our method on four different categories of combinatorial optimization problems and show that our approach performs strongly compared to the state-of-the-art machine learning and heuristics based methods.

关键词： Deep learning Systematics Monte Carlo methods NP-hard problem Input variables Reinforcement learning pattern recognition

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Advanced data mining and Applications 2012

引用

丛书名： Lecture Notes in Computer Science

2012年

作者： Shuigeng Zhou Songmao Zhang George Karypis

ISBN: (数字)9783642355271

ISBN: (纸本)9783642355264

this book constitutes the refereed proceedings of the 8th international conference on Advanced data mining and Applications, ADMA 2012, held in Nanjing, China, in December 2012. the 32 regular papers and 32 short papers presented in this volume were carefully reviewed and selected from 168 submissions. they are organized in topical sections named: social media mining; clustering; machine learning: algorithms and applications; classification; prediction, regression and recognition; optimization and approximation; mining time series and streaming data; Web mining and semantic analysis; data mining applications; search and retrieval; information recommendation and hiding; outlier detection; topic modeling; and data cube computing.

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Integrating Weight with Ensemble to Handle Changes in Class Distribution

Integrating Weight with Ensemble to Handle Changes in Class ...

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10th international conference on machine learning and data mining (mldm)

作者： Limsetto, Nachai Waiyamai, Kitsana Kasetsart Univ Fac Engn Dept Comp Engn Bangkok 10900 Thailand

ISBN: (纸本)9783319089799;9783319089782

Concept drift can be considered as a distribution mismatch problem where class distribution changes as a time passes. this problem is commonly found in classification task of data mining. Among the proposed solutions, the cost-based Class Distribution Estimation (CDE) shows the best performance in coping with difference in class distribution between train and test datasets. However there is still some problem, as CDE lost its performance when there is too much change in class distribution. In this paper, CDE-weight is proposed to reduce the impact of high change in class distribution. the idea is to use many models suitable with many class distributions along with dynamic weighting method that adjusts weight of each model according to its class distribution. Experimented results indicate that CDE-Weight methods are able to reduce the impact of misestimating and improve the classifier performance when train and test data are different.

关键词： Concept drift Classification Cost-sensitive learning Quantification Class distribution estimation Ensemble method

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Discriminant subspace learning based on support vectors machines

Discriminant subspace learning based on support vectors mach...

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8th international conference on machine learning and data mining in pattern recognition, mldm 2012

作者： Pitelis, Nikolaos Tefas, Anastasios School of Electronic Engineering and Computer Science Queen Mary University of London United Kingdom Department of Informatics Aristotle University of Thessaloniki Greece

ISBN: (纸本)9783642315367

A new method for dimensionality reduction and feature extraction based on Support Vector machines and minimization of the within-class data dispersion is proposed. An iterative procedure is proposed that successively applies Support Vector machines on perpendicular subspaces using the deflation transformation in such a way that the within-class variance is minimized. the proposed approach is proved to be a successive SVM using deflation kernels. the normal vectors of the successive hyperplanes contain discriminant information and they can be used as projection vectors for feature extraction and dimensionality reduction of the data. Experiments on various datasets are conducted in order to highlight the superior performance of the proposed algorithm. © 2012 Springer-Verlag.

关键词： Support vector machines

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Dynamic data Augmentation with Gating Networks for Time Series recognition 26

Dynamic Data Augmentation with Gating Networks for Time Seri...

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26th international conference on pattern recognition / 8th international Workshop on Image mining - theory and Applications (IMTA)

作者： Oba, Daisuke Matsuo, Shinnosuke Iwana, Brian Kenji Kyushu Univ Dept Adv Informat Technol Fukuoka Japan

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

data augmentation is a technique to improve the generalization ability of machine learning methods by increasing the size of the dataset. However, since every augmentation method is not equally effective for every dataset, you need to select an appropriate method carefully. We propose a neural network that dynamically selects the best combination of data augmentation methods using a Gating Network and a mutually beneficial feature consistency loss. the Gating Network is able to control how much of each data augmentation is used for the representation within the network. the feature consistency loss gives a constraint that augmented features from the same input pattern should be in similar. In the experiments, we demonstrate the effectiveness of the proposed method on the 12 largest time-series datasets from 2018 UCR Time Series Archive and reveal the relationships between the data augmentation methods through analysis of the proposed method.

关键词： Histograms Analytical models Time series analysis Neural networks Radar machine learning pattern recognition

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Discovery of New Multi-Level Features for Domain Generalization via Knowledge Corruption 26

Discovery of New Multi-Level Features for Domain Generalizat...

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26th international conference on pattern recognition / 8th international Workshop on Image mining - theory and Applications (IMTA)

作者： Frikha, Ahmed Krompass, Denis Tresp, Volker Siemens Technol Mumbai Maharashtra India Univ Munich LMU Munich Germany

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

machine learning models that can generalize to unseen domains are essential when applied in real-world scenarios involving strong domain shifts. We address the challenging domain generalization (DG) problem, where a model trained on a set of source domains is expected to generalize well in unseen domains without any exposure to their data. the main challenge of DG is that the features learned from the source domains are not necessarily present in the unseen target domains, leading to performance deterioration. We assume that learning a richer set of features is crucial to improve the transfer to a wider set of unknown domains. For this reason, we propose COLUMBUS, a method that enforces new feature discovery via a targeted corruption of the most relevant input and multi-level representations of the data. We conduct an extensive empirical evaluation to demonstrate the effectiveness of the proposed approach which achieves new state-of-the-art results by outperforming 18 DG algorithms on multiple DG benchmark datasets in the DOMAINBED framework.

关键词： Training machine learning Benchmark testing data models pattern recognition

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Online Adaptive Metrics for Model Evaluation on Non-representative Offline Test data 26

Online Adaptive Metrics for Model Evaluation on Non-represen...

引用

26th international conference on pattern recognition / 8th international Workshop on Image mining - theory and Applications (IMTA)

作者： Piovano, Enrico Le, Dieu-thu Chen, Bei Bradford, Melanie Amazon Com Inc Bellevue WA 98004 USA

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

A major challenge encountered in the offline evaluation of machine learning models before being released to production is the discrepancy between the distributions of the offline test data and of the online data, due to, e.g., biased sampling scheme, data aging issues and occurrence(s) of regime shift. Consequently, the offline evaluation metrics often do not reflect the actual performance of the model online. In this paper, we propose online adaptive metrics, a computationally efficient method which re-weights the offline metrics based on calculating the joint distributions of the model hypotheses over the offline test data VS. the online data. It provides offline metrics which estimate the production performance of the model by taking into account the test data biases. the proposed method is demonstrated by real life examples on text classification and a commercial natural language understanding system. We show that the online adaptive metrics can provide accurate predictions of online recall and precision even with a small test dataset.

关键词： Measurement Adaptation models Computational modeling Text categorization Production machine learning data models

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A Comparative Analysis of the Different data mining Tools by Using Supervised learning Algorithms 8th

A Comparative Analysis of the Different Data Mining Tools by...

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8th international conference on Soft Computing and pattern recognition (SoCPaR) / 8th international conference on Computational Aspects of Social Networks (CASoN)

作者： Goyal, Akarsh Khandelwal, Ishan Anand, Rahul Srivastava, Anan Swarnalatha, P. VIT Univ Sch Comp Sci & Engn Vellore 632014 Tamil Nadu India

ISBN: (纸本)9783319606187;9783319606170

these days a lot of raw data is generated from various common sources. this large amount of data, which would appear useless at first glance, is very important for companies and researchers as could provide a lot of helpful information. the data could be mined to get useful knowledge that could be used to make fruitful decisions. A lot of online tools and proprietary toolkits are available to the users and it becomes all the more cumbersome for them to know which is the best tool among these for the supervised learning algorithm and datasets they are applying. In order to aid this process, the paper progresses in this direction by doing a comparison of various data mining tools on the basis of their classification finesse. the various tools used in the paper are weka, knime and tanagra. Rigorous work on this has given the result that the performance of the tools is affected by the kind of datasets used and the way in which the supervised learning is done.

关键词： Weka Tanagra Knime mining tools Supervised learning

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Proximity-graph instance-based learning, support vector machines, and high dimensionality: An empirical comparison

Proximity-graph instance-based learning, support vector mach...

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8th international conference on machine learning and data mining in pattern recognition, mldm 2012

作者： Toussaint, Godfried T. Berzan, Constantin Faculty of Science New York University Abu Dhabi Abu Dhabi United Arab Emirates Department of Computer Science Tufts University Medford MA 02155 United States

ISBN: (纸本)9783642315367

Previous experiments with low dimensional data sets have shown that Gabriel graph methods for instance-based learning are among the best machine learning algorithms for pattern classification applications. However, as the dimensionality of the data grows large, all data points in the training set tend to become Gabriel neighbors of each other, bringing the efficacy of this method into question. Indeed, it has been conjectured that for high-dimensional data, proximity graph methods that use sparser graphs, such as relative neighbor graphs (RNG) and minimum spanning trees (MST) would have to be employed in order to maintain their privileged status. Here the performance of proximity graph methods, in instance-based learning, that employ Gabriel graphs, relative neighborhood graphs, and minimum spanning trees, are compared experimentally on high-dimensional data sets. these methods are also compared empirically against the traditional k-NN rule and support vector machines (SVMs), the leading competitors of proximity graph methods. © 2012 Springer-Verlag.

关键词： Support vector machines

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Solar Flare Forecasting with Deep learning-based Time Series Classifiers 26

Solar Flare Forecasting with Deep Learning-based Time Series...

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26th international conference on pattern recognition / 8th international Workshop on Image mining - theory and Applications (IMTA)

作者： Ji, Anli Wen, Junzhi Angryk, Rafal Aydin, Berkay Georgia State Univ Dept Comp Sci Atlanta GA 30302 USA

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Over the past two decades, machine learning and deep learning techniques for forecasting solar flares have generated great impact due to their ability to learn from a high dimensional data space. However, lack of high quality data from flaring phenomena becomes a constraining factor for such tasks. One of the methods to tackle this complex problem is utilizing trained classifiers with multivariate time series of magnetic field parameters. In this work, we compare the exceedingly popular multivariate time series classifiers applying deep learning techniques with commonly used machine learning classifiers (i.e., SVM). We intend to explore the role of data augmentation on time series oriented flare prediction techniques, specifically the deep learning-based ones. We utilize four time series data augmentation techniques and couple them with selected multivariate time series classifiers to understand how each of them affects the outcome. In the end, we show that the deep learning algorithms as well as augmentation techniques improve our classifiers performance. the resulting classifiers' performance after augmentation outplayed the traditional flare forecasting techniques.

关键词： solar flare prediction deep neural networks data augmentation

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